Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Nitrogen mineralization in straw from herbicide-desiccated millet

Pesquisas recentes verificaram que a utilização de glyphosate ou amônio glufosinate para dessecação de culturas de cobertura em sistemas conservacionistas podem reduzir o conteúdo de nitrogênio na palha formada. Neste contexto. O objetivo deste trabalho foi avaliar a mineralização do N da palhada de milheto em função da utilização dos herbicidas glyphosate e glufosinato de amônio e o aproveitamento do N pela cultura do milho. Utilizou-se o delineamento inteiramente casualizado, com seis tratamentos e cinco repetições, em esquema fatorial 3 x 2, sendo avaliados os fatores: manejo da cultura de cobertura (testemunha sem aplicação de herbicida, dessecação com glyphosate ou glufosinato de amônio) e tipo de solo (Neossolo Quartzarênico órtico típico - NQ e Latossolo Vermelho distrófico típico - LV). A massa de palha seca remanescente nos solos foi maior quando o milheto foi dessecado quimicamente em relação à testemunha. O N-mineral do solo proveniente da palha de milheto, o C-biomassa microbiana e a nitrificação nos tratamentos em que o milheto foi dessecado com herbicidas foram inferiores aos da testemunha. A utilização de dessecantes no milheto não afetou a produção de massa de palha seca e o N-total do milho, embora o conteúdo de N proveniente da palha no milho tenha sido reduzido pela dessecação do milheto com o herbicida glufosinato. A mineralização do N proveniente da palha foi reduzida pela dessecação do milheto com os herbicidas glyphosate e glufosinato de amônio.Currently research verified that glyphosate or ammonium glufosinate herbicides use for cover crop desiccation in conservationist systems may reduce N amount in straw. The aim of this study was to evaluate nitrogen mineralization in millet straw after glyphosate or ammonium glufosinate application and the N uptake by corn plants. A complete randomized design was used with six treatments and five replications, in a 3 x 2 factorial scheme: cover crop management (without herbicide application, glyphosate or ammonium glufosinate desiccation) and soil type (Typic Quartzipsamment and Red Oxisol). The residual millet straw dry matter on the soil surface was higher after herbicide desiccation than in the control. The soil mineral N, originated from millet straw, the microbial biomass C and nitrification were lower in herbicide-desiccated millet than in the control. Chemical millet desiccation did not affect the dry matter production and total N of the following corn crop, although the corn nitrogen content, derived from the straw, was reduced by millet desiccation with glufosinate herbicide. The nitrogen mineralization of millet straw was reduced by herbicide desiccation with glyphosate or glufosinate

Health-status outcomes with invasive or conservative care in coronary disease

BACKGROUND In the ISCHEMIA trial, an invasive strategy with angiographic assessment and revascularization did not reduce clinical events among patients with stable ischemic heart disease and moderate or severe ischemia. A secondary objective of the trial was to assess angina-related health status among these patients. METHODS We assessed angina-related symptoms, function, and quality of life with the Seattle Angina Questionnaire (SAQ) at randomization, at months 1.5, 3, and 6, and every 6 months thereafter in participants who had been randomly assigned to an invasive treatment strategy (2295 participants) or a conservative strategy (2322). Mixed-effects cumulative probability models within a Bayesian framework were used to estimate differences between the treatment groups. The primary outcome of this health-status analysis was the SAQ summary score (scores range from 0 to 100, with higher scores indicating better health status). All analyses were performed in the overall population and according to baseline angina frequency. RESULTS At baseline, 35% of patients reported having no angina in the previous month. SAQ summary scores increased in both treatment groups, with increases at 3, 12, and 36 months that were 4.1 points (95% credible interval, 3.2 to 5.0), 4.2 points (95% credible interval, 3.3 to 5.1), and 2.9 points (95% credible interval, 2.2 to 3.7) higher with the invasive strategy than with the conservative strategy. Differences were larger among participants who had more frequent angina at baseline (8.5 vs. 0.1 points at 3 months and 5.3 vs. 1.2 points at 36 months among participants with daily or weekly angina as compared with no angina). CONCLUSIONS In the overall trial population with moderate or severe ischemia, which included 35% of participants without angina at baseline, patients randomly assigned to the invasive strategy had greater improvement in angina-related health status than those assigned to the conservative strategy. The modest mean differences favoring the invasive strategy in the overall group reflected minimal differences among asymptomatic patients and larger differences among patients who had had angina at baseline